Configuring Motion Planning for a Self-Driving Tractor Unit

ABSTRACT

A control system of a self-driving tractor can access sensor data to determine a set of trailer configuration parameters of a cargo trailer coupled to the self-driving tractor. Based on the set of trailer configuration parameters, the control system can configure a motion planning model for autonomously controlling the acceleration, braking, and steering systems of the tractor.

BACKGROUND

Operation of semi-trailer trucks requires intensive training tonegotiate turns, perform reversing maneuvers, navigate narrow cityroads, handle traffic congestion, and performing parking and dockingmaneuvers with adequate clearance to safely avoid obstacles. Operatorsof semi-trailer trucks are typically required to attend a trainingschool or program certified by the Professional Truck Driver Institute(PTDI), and must pass written knowledge exams and skills tests toreceive a commercial driver's license. A primary reason for suchextensive training is the complexity of safely setting up andcontrolling a cargo trailer typically coupled to the fifth wheel of thetractor unit.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements, and in which:

FIG. 1 illustrates a self-driving semi-trailer truck including aself-driving tractor unit coupled to a cargo trailer, according toexamples described herein;

FIG. 2 is a block diagram illustrating an example self-driving tractorunit implementing an autonomous vehicle control system, as describedherein;

FIG. 3 is a block diagram illustrating an example trailer monitoringsystem in communication with a motion planning engine of a vehiclecontrol system, according to various example embodiments;

FIG. 4 is a flow chart illustrating an example method of configuring amotion planning model for autonomous operation of a self-driving tractorunit, according to example embodiments described herein;

FIG. 5 is a flow chart illustrating another example method ofconfiguring a motion planning model for autonomous operation of aself-driving tractor unit, according to example embodiments describedherein; and

FIG. 6 is a block diagram illustrating a computer system upon whichexample processing systems of a self-driving tractor unit describedherein may be implemented.

DETAILED DESCRIPTION

A self-driving tractor unit for a semi-trailer truck is describedherein. The self-driving tractor unit can include sensors mounted in oneor more sensors arrays on the exterior of the self-driving tractor unit,such as monocular or stereo cameras, LIDAR sensors, proximity sensors,infrared sensors, sonar sensors, and the like. The self-driving tractorunit can also include a control system comprising processing resources,such as field programmable gate arrays and CPUs, that couple to operatethe various control mechanisms of the self-driving tractor unit. Thesecontrol mechanisms can include the tractor unit's acceleration, braking,and steering systems, as well as the various signaling, shifting, andlighting systems.

A number of the sensors may be rearward facing, or have fields of viewthat include the cargo trailer or a portion of the cargo trailer. Forexample, a top rearward-facing camera can generate image dataidentifying the full length of the top of the cargo trailer, while alower rearward facing camera extending from the front bumper of thetractor unit can detect an axle or bogie position of the cargo trailer.Additionally or alternatively, one or more of LIDAR sensors mounted torespective booms of a rooftop sensor array of the tractor unit cangenerate detailed LIDAR sensor data that indicates the dimensions of thecargo trailer as well as the location of the bogie and axle positions.Based at least partially on sensor data from the sensors, the controlsystem can determine a set of trailer configuration parameters of acargo trailer coupled to the self-driving tractor unit. Based on thetrailer configuration parameters, the control system can modify a motionplanning model for autonomously controlling the acceleration, braking,and steering systems of the tractor unit.

The trailer configuration parameters can comprise a set of parametersthat can affect the trajectory of the cargo trailer as it is pulled orpushed by the tractor unit (e.g., around sharp turn or during dockingprocedures). In certain examples, the trailer configuration parameterscan comprise the kingpin overhang length of the cargo trailer (e.g., thebogie slider position), initial trailer angle, trailer shapecharacteristics, and a length, width, and/or height of the cargo trailerto determine its clearance values. Additionally or alternatively, theconfiguration parameters can include the type of trailer (e.g.,refrigerated, auto-carrier, flat-bed, hazardous material, etc.), thenumber of wheels and axles of the rear bogie, the number of trailers andbogies (e.g., for double or triple trailers), the tire compounds and/ortread pattern, certain aerodynamic features of the cargo trailer (e.g.,lower trailer fairings or skirts, gap devices, nose cones, wheel covers,bogie fairings, rear boat tails, etc.), the trailer weight, cargoarrangement within or on top of the trailer, the center of gravity ofthe trailer, and the like. Such trailer configuration parameters can bedirectly observable by the sensor systems of the tractor unit, or may beinferred, or trailer-specific information may be determined via adatabase lookup or received from a remote trailer tracking or managementresource.

Once the configuration parameters of the cargo trailer are determined,the control system of the tractor unit can modify or configure a motionplanning model that controls the manner in which the control systemautonomously operates the control mechanisms of the tractor unit (e.g.,the acceleration, braking, and steering systems). As an example, thetrailer length and rear bogie position can affect how wide the tractorunit must take turns in order to provide adequate clearance to curbs,proximate vehicles, pedestrians, and other traffic lanes. Furthermore,the presence of rear boat tails and other aerodynamic features on thecargo trailer can affect docking procedures. Still further, the cargoweight distribution, thrust line offset or axle misalignment, thecurrent road conditions and weather conditions, and the type of cargotrailer can also affect the manner in which the control system modifiesits motion planning model. For example, the control system can increaseclearance requirements for a trailer carrying hazardous materials, suchas gasoline, propane, or liquid nitrogen. Accordingly, the controlsystem can computationally optimize its motion planning model based onthe various detected or determine trailer configuration parameters.

In some aspects, the control system can autonomously drive the tractorunit around the cargo trailer prior to hookup to collect sensor datacorresponding to the trailer configuration parameters. Additionally, thecollected sensor data can provide identifying information of the cargotrailer, such as the trailer's vehicle identification number (VIN) orlicense plate number. In variations, when the tractor unit is hooked upto the cargo trailer, the control system can receive, over a data bus,the identifying information from the cargo trailer (e.g., by connectingwith a memory device of the cargo trailer). In further variations, thecontrol system of the tractor unit can receive the identifyinginformation of the cargo trailer from remote resource, such as a backendcargo trailer tracking, coordination, and management system (e.g.,operating on remote servers of a datacenter).

Using the identifying information of the cargo trailer, the controlsystem can perform a lookup for additional configuration parameters ofthe cargo trailer. The lookup may be performed in a local database ofthe tractor unit or a remote resource. In certain implementations, byperforming the lookup, the control system can determine generalinformation concerning the physical dimensions and setup of the trailer,or for more dynamic trailer tracking and coordination implementations,the additional configuration parameters can include the cargo, cargoweight, and cargo distribution within the trailer.

Among other benefits, the examples described herein achieve a technicaleffect of providing autonomous self-driving tractor units with specificcargo trailer information to enable the tractor unit to operate moreeffectively and efficiently when pulling cargo trailers. When theself-driving tractor unit has the trailer configuration parametersspecific to an individual cargo trailer, a more precise motion planningmodel can be generated and executed by the control system of the tractorunit to ensure adequate clearance to objects and entities duringoperation, proper docking execution, and maintaining the cargo trailerin a safe state.

As used herein, a computing device refers to devices corresponding todesktop computers, cellular devices or smartphones, personal digitalassistants (PDAs), laptop computers, tablet devices, virtual reality(VR) and/or augmented reality (AR) devices, wearable computing devices,television (IP Television), etc., that can provide network connectivityand processing resources for communicating with the system over anetwork. A computing device can also correspond to custom hardware,in-vehicle devices, or on-board computers, etc. The computing device canalso operate a designated application configured to communicate with thenetwork service.

One or more examples described herein provide that methods, techniques,and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device. A programmatically performed step mayor may not be automatic. An action performed automatically, as usedherein, means the action is performed without necessarily requiringhuman intervention.

One or more examples described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming one or more stated tasks or functions. As used herein, amodule or component can exist on a hardware component independently ofother modules or components. Alternatively, a module or component can bea shared element or process of other modules, programs or machines.

Some examples described herein can generally require the use ofcomputing devices, including processing and memory resources. Forexample, one or more examples described herein may be implemented, inwhole or in part, on computing devices such as servers, desktopcomputers, cellular or smartphones, personal digital assistants (e.g.,PDAs), laptop computers, printers, digital picture frames, networkequipment (e.g., routers) and tablet devices. Memory, processing, andnetwork resources may all be used in connection with the establishment,use, or performance of any example described herein (including with theperformance of any method or with the implementation of any system).

Furthermore, one or more examples described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a computer-readablemedium. Machines shown or described with figures below provide examplesof processing resources and computer-readable mediums on whichinstructions for implementing examples disclosed herein can be carriedand/or executed. In one embodiment, a software module is implementedwith a computer program product including a computer-readablenon-transitory medium containing computer program code, which can beexecuted by a computer processor for performing any or all of the steps,operations, or processes described. As such, one or more general purposeprocessors coupled to the computer-readable medium correspond to aspecial purpose processor system for performing the steps, operations,or processes described herein. In particular, the numerous machinesshown with examples of the invention include processors and variousforms of memory for holding data and instructions. Examples ofcomputer-readable mediums include permanent memory storage devices, suchas hard drives on personal computers or servers. Other examples ofcomputer storage mediums include portable storage units, such as CD orDVD units, flash memory (such as those carried on smartphones,multifunctional devices or tablets), and magnetic memory. Computers,terminals, network enabled devices (e.g., mobile devices, such as cellphones) are all examples of machines and devices that utilizeprocessors, memory, and instructions stored on computer-readablemediums. Additionally, examples may be implemented in the form ofcomputer-programs, or a computer usable carrier medium capable ofcarrying such a program.

Numerous examples are referenced herein in context of a self-drivingvehicle. A self-driving vehicle refers to a vehicle that is operated ina state of automation with respect to steering and propulsion. Differentlevels of autonomy may exist with respect to self-driving vehicles. Forexample, some vehicles may enable automation in limited scenarios, suchas on highways, provided that drivers are present in the vehicle. Moreadvanced self-driving vehicles can drive without any human assistancefrom within or external to the vehicle.

Example Self-Driving Semi-Trailer Trucks

FIG. 1 illustrates a self-driving semi-trailer truck including aself-driving tractor unit coupled to a cargo trailer, according toexamples described herein. As shown in FIG. 1, the self-drivingsemi-trailer truck 100 can include a self-driving tractor unit 110 witha cargo trailer 120 having a kingpin coupled to a fifth wheel or trailerhitch of the self-driving tractor 110. The self-driving tractor 110includes a number of sensor arrays 106, 108 each including any number ofsensors and sensor types. For example, sensor array 106 can include aprimary LIDAR sensor 112 and a number of additional LIDAR sensors 114, anumber of cameras 116, and other sensors, such as a radar system,proximity sensors, infrared sensors, sonar sensors, and the like. Thevarious sensors of the sensor arrays 106, 108 can provide an autonomouscontrol system of the self-driving tractor 110 with a fused sensor viewof a surrounding environment of the self-driving semi-trailer truck 100to enable the control system to autonomous operate the controlmechanisms of the self-driving tractor 110, as described in detailbelow.

The cargo trailer 120 can be any type of trailer that couples to thefifth wheel or trailer hitch of the tractor 100, and can carry any typeof cargo. For example, the cargo trailer 120 can comprise a standard boxtrailer, a car-carrier trailer, a flatbed trailer, a tanker trailer, adump trailer, a hopper bottom trailer, a lowboy trailer, a refrigerationtrailer, a tank container chassis trailer, or a double trailer. Ingeneral, the cargo trailer 120 can comprise a cargo carrier 124, a rearbogie 118, and a bogie slider 122 configured to change the position ofthe rear bogie 118 in relation to the cargo carrier 124. In certainimplementations, the bogie slider 122 is manually configured to positionthe rear bogie 118 beneath the cargo carrier 124. For example, while thecargo carrier 124 is coupled to the fifth wheel of the tractor 110, andwith the bogie slider 122 disengaged, the self-driving tractor 110 canautonomously drive forward or rearward to reposition the rear bogie 118in relation to the cargo carrier 124. Thereafter, the bogie slider 122can be engaged to affix the rear bogie 118 to the cargo carrier 124 atthe desired position.

In variations, the rear bogie 118 can be automatically repositionedbeneath cargo carrier 124. For example, at least one of the rear bogie118, the cargo carrier 124, or the bogie slider 122 can include a linearmotor, such as a linear electric motor, operable by a positioncontroller that can operate the linear motor to reposition the rearbogie 118 beneath the cargo carrier 124. In such variations, theposition controller of the rear bogie 118 and/or cargo carrier 124 canbe coupled to the control system of the self-driving tractor 110 (e.g.,upon kingpin hookup of the cargo trailer 120 to the fifth wheel), andthe control system of the tractor 110 can manipulate the position of therear bogie 118 beneath the cargo carrier 124 (e.g., based on a weightdistribution of cargo within the cargo carrier 124 or a determinedcenter of gravity of the cargo trailer 120).

According to examples described herein, the control system of thetractor 110 can actively position the rear bogie 118 beneath the cargocarrier 124 based on information determined through monitoring sensordata identifying the initial configuration of the cargo trailer 120,and/or receiving or looking up trailer-specific information of thecargo-trailer 120 (e.g., a total weight, cargo weight, cargodistribution, or weight distribution). For example, based on suchinformation, the control system of the self-driving tractor 110 candetermine an optimal position for the rear bogie 118 beneath the cargocarrier 124, and through active autonomous operation of the positioncontroller(s) of the rear bogie 118 and/or cargo carrier 124, thecontrol system can configure the optimal position of the rear bogie 118.Once the rear bogie 118 is in the desired or optimized position beneaththe cargo carrier 124, the control system of the tractor 110 can engagedor lock the bogie slider 122 in place. With the rear bogie 118 optimallyconfigured, the control system of the tractor 110 may then modify amotion planning model for operating the acceleration, braking, andsteering systems of the self-driving tractor 110 for autonomouslyoperating the self-driving semi-trailer truck 100.

In certain implementations, the rear bogie 118 may be pre-positionedunderneath the cargo trailer 120 when the cargo trailer 120 is coupledto the self-driving tractor 110. In such examples, the control system ofthe tractor 110 can analyze sensor data from the various sensors of thetractor 110 to identify the position of the rear bogie 118 or itsindividual axles. Rear bogies typically employ a dual axleconfiguration, with a standardized spacing between the axles.Accordingly, utilizing the sensor data, the control system of thetractor 110 can identify a position of a near axle, or first axle of therear bogie 118, and then infer the position of the far axle, or secondaxle of the rear bogie 118. Additionally or alternatively, the controlsystem can also analyze the sensor data to determine the length, width,and height of the cargo carrier 124 as well as other configurations,such as any aerodynamic features. These aerodynamic features can includelower trailer fairings or skirts, gap devices, nose cones, wheel covers,bogie fairings, rear boat tails, and the like. Examples described hereinrecognize that certain aerodynamic features can affect certain operativecharacteristics of the semi-trailer truck 100, such as handlingcharacteristics, fuel mileage, docking clearance, and kingpin overhang.Any one or more of these operative characteristics can be factored intothe motion planning model generated and employed by the control systemin autonomously operating the self-driving tractor 110.

In certain examples, the control system of the tractor 110 can determineadditional configuration parameters of the cargo trailer 120. Forexample, the control system can analyze the sensor data to determine thetype of cargo carrier 124 and/or the number of cargo carriers 124 andbogies. Example types of cargo carriers 124 can include a standard boxtrailer (as shown in FIG. 1), a car-carrier trailer, a flatbed trailer,a tanker trailer, a dump trailer, a hopper bottom trailer, a lowboytrailer, a refrigeration trailer, a tank container chassis trailer, or adouble or triple trailer. In certain scenarios, the control system ofthe tractor 110 can further infer a weight of the cargo trailer 120,either based on direct analysis of the sensor data (e.g., observing afull load of vehicles on a car-carrier trailer), or based on the torqueor force necessary to pull the cargo trailer 120. For example, a straingauge or mechanical force sensor may be included in the fifth wheel tomeasure the dynamic pull force of the cargo trailer 120, and the controlsystem can extrapolate the weight of the cargo trailer 120 based on themeasured forces.

In certain examples, the control system of the tractor 110 can receiveor otherwise determine additional information concerning theconfiguration parameters of the cargo trailer 120. In some aspects, thecontrol system of the tractor 110 can receive trailer configurationinformation directly from a memory resource of the cargo trailer 120when the cargo trailer 120 is hooked up via a wired connection to thetractor 110 (e.g., via a data pin or power pin of a round pinconnector). In variations, certain configuration parameters of the cargotrailer 120 may be imprinted on scannable resources, such as RFID chipsor a QR code, and can be manually scanned and uploaded or otherwiseprovided to the control system of the self-driving tractor 110 to modifyits motion planning model. In other examples, the control system of thetractor 110 can include network connectivity resources, such as aBluetooth, Wi-Fi, cellular, or other wireless radio-frequencycommunication module that communicates with a backend trailercoordination system operating on a remote datacenter, which can providecertain trailer configuration parameters and other information relatedto the cargo trailer 120, such as the cargo being carried and the cargomass. Other trailer configuration parameters that can be communicated tothe control system of the tractor 110 can include the wheel size, tirecompounds and/or tire treads, which can affect the handling of the cargotrailer 120 in inclement weather.

Based on any one or more of the trailer configuration parametersdescribed herein, the control system of the tractor 110 can modify itsmotion planning model accordingly. In addition to modifying the motionplanning model, the control system of the tractor 110 can also monitorthe cargo trailer 120 as the self-driving semi-trailer truck 100operates throughout its given region. Live monitoring of the cargotrailer 120 can enable the control system to determine, for example,actual clearances, thrust line offset or axle misalignment of the rearbogie, any tire blowouts, etc. Thus, the motion planning model employedby the control system can also be modified dynamically based on the livemonitoring of the cargo trailer 120. As described below, the motionplanning model executed by the control system determines the manner inwhich the control system operates the acceleration, braking, steering,and other control mechanisms of the tractor 110.

Example Systems

FIG. 2 is a block diagram illustrating an example self-driving tractorimplementing an autonomous vehicle control system, according to examplesdescribed herein. In an example of FIG. 2, a vehicle control system 220can autonomously operate the self-driving tractor 200 throughoutgeographic regions for a variety of purposes, including transportservices (e.g., on-demand transport, freight and delivery services,etc.). In examples described, the self-driving tractor 200 can operateautonomously without human control. For example, the self-drivingtractor 200 can autonomously steer, accelerate, shift, brake, andoperate lighting components. Some variations also recognize that theself-driving tractor 200 can switch between an autonomous mode, in whichthe vehicle control system 220 autonomously operates the tractor 200,and a manual mode in which a qualified driver takes over manual controlof the acceleration system 272, steering system 274, braking system 276,and lighting and auxiliary systems 278 (e.g., directional signals andheadlights).

According to some examples, the vehicle control system 220 can utilizespecific sensor resources 210 to autonomously operate the tractor 200 ina variety of driving environments and conditions. For example, thevehicle control system 220 can operate the tractor 200 by autonomouslyoperating the steering, acceleration, and braking systems 272, 274, 276of the tractor 200 to a specified destination. The control system 220can perform low-level vehicle control actions (e.g., braking, steering,accelerating) and high-level route planning using sensor information, aswell as other inputs (e.g., transmissions from remote or local humanoperators, network communication from other vehicles, a freighttransport coordination system, etc.).

In an example of FIG. 2, the vehicle control system 220 includescomputational resources (e.g., processing cores and/or fieldprogrammable gate arrays (FPGAs)) which operate to process sensor datareceived from a sensors 210 of the tractor 200 that provides a sensorview of a road segment upon which the tractor 200 operates. The sensordata can be processed to determine actions which are to be performed bythe tractor 200 in order for the tractor 200 to continue on a route tothe destination, or in accordance with a set of transport instructionsreceived from a remote freight transport coordination service. In somevariations, the vehicle control system 220 can include otherfunctionality, such as wireless communication capabilities using acommunications module, to send and/or receive wireless communicationsover one or more networks 275 with one or more remote sources. Incontrolling the tractor 200, the control system 220 can generatecommands to control the various vehicle control mechanisms 270 of thetractor 200, including the acceleration system 272, steering system 274,braking system 276, and auxiliary systems 278 (e.g., lights anddirectional signals).

The self-driving tractor 200 can be equipped with multiple types ofsensors 210 which can combine to provide a computerized perception, orsensor view, of the space and the physical environment surrounding thetractor 200. Likewise, the control system 220 can operate within thetractor 200 to receive sensor data from the sensors 210 and to controlthe various vehicle controls 270 in order to autonomously operate thetractor 200. For example, the control system 220 can analyze the sensordata to generate low level commands executable by the accelerationsystem 272, steering system 274, and braking system 276 of the tractor200. Execution of the commands by the control mechanisms 270 can resultin throttle inputs, braking inputs, and steering inputs thatcollectively cause the tractor 200 to operate along sequential roadsegments according to a route plan.

In more detail, the sensors 210 operate to collectively obtain a livesensor view for the vehicle control system 220 (e.g., in a forwardoperational direction, or providing a 360 degree sensor view), and tofurther obtain situational information proximate to the tractor 200,including any potential hazards or obstacles. By way of example, thesensors 210 can include a positioning system 212, such as a GPS module,and object detection sensors 214. The object detection sensors 214 canbe arranged in a sensor suite or sensor arrays mounted to the exteriorof the tractor 200, such as on the front bumper and roof. The objectdetection sensors 214 can comprise multiple sets of cameras (videocameras, stereoscopic cameras or depth perception cameras, long rangemonocular cameras), LIDAR sensors, one or more radar sensors, andvarious other sensor resources such as sonar, proximity sensors,infrared sensors, and the like.

In general, the sensors 210 collectively provide sensor data to aperception engine 240 of the vehicle control system 220. The perceptionengine 240 can access a data storage 230 comprising localizationsub-maps of the given region in which the tractor 200 operates. Thelocalization sub-maps can comprise a series of road segment sub-mapsthat enable the perception engine 240 to perform dynamic comparisonswith the live sensor view to perform object detection operations. Asprovided herein, the localization sub-maps can comprise highly detailedground truth data of each road segment on which the self-driving tractor200 can travel. For example, the localization sub-maps can encompasslong stretches of highways where perception operations are relativelyundemanding compared to a crowded urban environment. The localizationsub-maps can comprise prerecorded and fused data (e.g., sensor dataincluding image data, LIDAR data, and the like) by specialized mappingvehicles or autonomous vehicles with recording sensors and equipment,and can be processed to pinpoint various objects of interest (e.g.,traffic signals, road signs, and other static objects). As the controlsystem 220 autonomously operates the tractor 200 along a given route,the perception engine 240 can access sequential localization sub-maps ofcurrent road segments to compare the details of a current localizationsub-map with the sensor data in order to detect and classify any objectsof interest, such as road debris, other vehicles, pedestrians,bicyclists, and the like.

In various examples, the perception engine 240 can dynamically comparethe live sensor data from the tractor's sensors 210 to the currentlocalization sub-map as the tractor 200 travels through a correspondingroad or highway segment. The perception engine 240 can identify andclassify any objects of interest in the live sensor data that canindicate a potential hazard. In accordance with many examples, theperception engine 240 can provide object of interest data to aprediction engine 245 of the control system 220, where the objects ofinterest can each be classified (e.g., a pedestrian, a bicyclist,unknown objects, other vehicles, a static object, etc.).

Based on the classification of the detected objects, the predictionengine 245 can predict a path of each object of interest and determinewhether the vehicle control system 220 should respond or reactaccordingly. For example, the prediction engine 245 can dynamicallycalculate a collision probability for each object of interest, andgenerate event alerts if the collision probability exceeds a certainthreshold. As described herein, such event alerts can be processed by amotion planning engine 260 along with a processed sensor view indicatingthe classified objects within the live sensor view of the tractor 200.The vehicle controller 255 can then generate control commands executableby the various vehicle controls 270 of the tractor 200, such as theacceleration, steering, and braking systems 272, 274, 276. In certainexamples, the motion planning engine 260 can determine an immediate, lowlevel trajectory and/or higher-level plan for the tractor 200 based onthe event alerts and processed sensor view (e.g., for the next 100meters, up to a next intersection, or for a certain distance along ahighway).

On a higher level, the motion planning engine 260 can provide thevehicle controller 255 with a route plan to a given destination, such asa pick-up location, a docking and drop off location, or otherdestination within a given road network. In various aspects, the motionplanning engine 260 can generate the route plan based on transportinstructions received from a remote freight coordination service (e.g.,over the network 275). On a lower level, the motion planning engine 260can provide the vehicle controller 255 with an immediate trajectory forthe tractor 200 based on the objects of interest, obstacles, andcollision probabilities identified and determined by the perception andprediction engines 240, 245. The vehicle controller 255 can generate theappropriate control commands executable by the vehicle controls 270accordingly.

In various examples, the motion planning engine 260 generatestrajectories for the tractor 200 in accordance with a motion planningmodel. Execution of the motion planning model enables the motionplanning engine 260 to safely calculate and/or construct trajectories inaccordance with the configuration and capabilities of the tractor 200,such as the maximum turning radius of the tractor 200, the dimensions ofthe tractor 200 (e.g., its overall length, width, and height), and theaxle positions of the tractor 200 (e.g., to determine how wide to take aparticular turn to ensure adequate clearance from curbs and objects).

According to various examples, whenever the tractor 200 is hooked up viaa wired connection to a cargo trailer, the motion planning engine 260must modify its motion planning model to account for the trajectory ofthe cargo trailer as well as the trajectory of the tractor 200. Thus,when the kingpin of a cargo trailer is coupled to the fifth wheel of thetractor 200, the motion planning engine 260 requires variousconfiguration parameters of the cargo trailer in order to accuratelymodify its motion planning model. In some aspects, the trailerconfiguration parameters necessary for the motion planning model cancomprise the dimensions of the cargo trailer, such as its height, width,length, and shape. Other trailer configuration parameters that can befactored into the motion planning model by the motion planning engine260 can include the kingpin overhang length of the cargo trailer, thebogie slider of the rear bogie, the axle positions, and initial trailerangle. Additionally or alternatively, the configuration parameters caninclude the type of trailer (e.g., refrigerated, auto-carrier, flat-bed,hazardous material, etc.), the number of wheels and axles of the rearbogie, the number of trailers and bogies (e.g., for double or tripletrailers), the tire compounds and/or tread pattern, aerodynamic featuresof the cargo trailer, the trailer weight, the cargo arrangement withinor on top of the trailer, the center of gravity of the trailer, and thelike. As described herein, the trailer configuration parameters can bedirectly observable via the sensors 210 of the tractor 200, or may bedetermined or inferred based on the sensor data. In variations, certaintrailer configuration parameters can be determined via a database lookupor received from a remote trailer coordination system 280 (e.g., overnetwork 275).

To determine the trailer configuration parameters, the vehicle controlsystem 220 can include a trailer monitoring system 280, which canreceive sensor data from one or more sensors 210 of tractor 200. Forexample, the tractor 200 can include a number of rearward facing cameras(e.g., attached on a boom of a rooftop sensor array, extending outwardfrom bumper sensor array, or mounted to the rear wall of the cab of thetractor 200). Additionally or alternatively, the tractor 200 can includea number of LIDAR sensors having a rearward field of view that candetect a top and/or side surface of the cargo trailer. In furtherimplementations, the tractor 200 can include a camera (monocular orstereo-camera) and/or a LIDAR sensor that can detect the rear bogieposition of the cargo trailer, which can be coupled to the rear centeraxle of the tractor 200 (e.g., near the license plate holder), or alower side position of the cab. In still further implementations, thesensors 210 of the tractor 200 can include a number force sensors (e.g.,a spring sensor) within the fifth wheel which can detect forces from thecargo trailer when it is being pulled by the tractor 200. As describedherein, the trailer monitoring system 280 can determine a weight of thecargo trailer based on such detected forces.

As further described herein, additional trailer configuration parameterscan be determined by the trailer monitoring system 280, such as thetrailer type. The trailer type can affect where the tractor 200 ispermitted to travel. For example, the trailer monitoring system 280 canidentify that the trailer type comprises a hazardous materials carrier,which may not be permitted on certain roads and areas. Thus, upondetecting the carrier type, the trailer monitoring system 280 can causethe motion planning engine 260 to block off certain routes throughimpermissible areas.

In certain implementations, the trailer monitoring system 280 canreceive certain information concerning the cargo trailer over acommunication network 275, or by performing a lookup in a local datastorage device 230. For example, the trailer monitoring system 280 canreceive identifying information corresponding to the cargo trailer, suchas the cargo trailer's vehicle identification number or license platenumber. In one example, the trailer monitoring system 280 receives theidentifying information from a trailer coordination system 280 over thenetwork 275. Additionally or alternatively, the trailer monitoringsystem 280 can receive additional information from the trailercoordination system 280, such as the cargo being carried, the cargoweight, and various configuration parameters of the cargo carrier orcargo trailer described herein. In variations, the trailer monitoringsystem 280 can detect the identifying information of the cargo trailerthrough the sensor data (e.g., an image of the license plate prior tohook-up). In further variations, the trailer monitoring system 280 canreceive the identifying information at the time of hook-up through adata bus connection with a memory resource of the cargo trailer.

Based on the identifying information, the trailer monitoring system 280can determine any number of configuration parameters of the cargotrailer. For example, the trailer coordination system 280 may includeupdated cargo logs and trailer characteristic logs for each trailer thatcan be hooked up to the tractor 200 via a wired connection. Accordingly,the trailer monitoring system 280 can transmit a data request comprisingthe identifying information for the coupled cargo trailer to the trailercoordination system 280, and receive the logged data corresponding tothe cargo trailer. These data can comprise any of the trailerconfiguration parameters described herein. In variations, the vehiclecontrol system 220 can store an updated trailer log that includesconfiguration data for each trailer in an available fleet of trailers.

It is contemplated that the coupling procedure between the tractor 200and the cargo trailer may be automated, including coupling and lockingbetween the kingpin and the fifth wheel of the tractor 200 and theelectrical coupling to supply power to the various lights of the cargotrailer. In some aspects, the vehicle control system 220 can implementan automatic coupling procedure to couple the fifth wheel to the kingpinof the cargo trailer and a power-to-power coupling with the cargotrailer. In one aspect, the electrical coupling can cause an initialdata transmission from a memory resource of the cargo trailer, which cancomprise certain trailer configuration information of the cargo trailer.

According to various examples, once the trailer configuration parametersare determined, the trailer monitoring system 280 can transmit theconfiguration parameters to the motion planning engine 260, which canconstruct a new motion planning model or otherwise modify a normal ordefault motion planning model in accordance with the trailerconfiguration parameters. The motion planning model can comprise a setof trajectory instructions for executing maneuvers in the overalloperation of the tractor 200 with the cargo trailer coupled thereto toensure safe clearance of the cargo trailer and its rear bogie fromcurbs, objects, pedestrians, traffic signs, lamp posts, other vehicles,docking facilities, overpasses, and other potential obstacles.

Once the motion planning model is updated by the motion planning engine260 in accordance with the detected, accessed, received, or determinedtrailer configuration parameters, the trailer monitoring system 280 canactively monitor the cargo trailer during autonomous operation of theself-driving tractor 200. In certain aspects, the trailer monitoringsystem 280 can determine whether the cargo trailer has a thrust offsetangle, or whether the respective toe angles of the rear bogie's wheelsare straight. Additionally, the trailer monitoring system 280 canactively monitor the side clearances of the cargo carrier and the pathof the rear bogie, and provide an alert or warning signal to the motionplanning engine if the actual trajectory veers from the plannedtrajectory beyond a tolerance buffer (e.g., ten inches from the plannedtrajectory). The warning signal can cause the motion planning engine 260to dynamically update the trajectory, and in some examples, furtherupdate the overall motion planning model.

FIG. 3 is a block diagram illustrating an example trailer monitoringsystem in communication with a motion planning engine of a vehiclecontrol system, according to various examples. The trailer monitoringsystem 300 and motion planning engine 370 shown and described withrespect to FIG. 3 can correspond to the trailer monitoring system 280and motion planning engine 260 as shown and described in connection withFIG. 2. Referring to FIG. 3, the trailer monitoring system 300 caninclude a sensor data analysis engine 330, which can analyze sensor datafrom sensor resources of the tractor to directly detect and determinecertain configuration parameters of the cargo trailer. Specifically, thesensor data analysis engine 330 can include a sensor interface 332 thatreceives image data, LIDAR data, kingpin force data, etc., and a dataextrapolator 334 to determine a set of trailer configuration parametersbased on the sensor data.

Example configuration parameters that may be directly determined by thedata extrapolator 334 can include the dimensions of the cargotrailer—such as the length, width, height, and shape of the trailer—thecargo itself (e.g., for auto-carriers, flatbed carriers, or hazardousmaterials carriers), the weight of the cargo trailer (e.g., determinedfrom force data), the rear bogie position and axle positions, the numberof wheels, tire treads, any aerodynamic features, and the kingpinoverhang. The sensor data analysis engine 330 may also detectidentifying information specific to the cargo trailer, such as thetrailer's license plate or vehicle identification number. A trajectoryoptimizer 380 of the motion planning engine 380 can parse the variousconfiguration parameters determined from the sensor data analysis engine330 to generate an updated motion planning model for the tractor'sautonomous control system for safely pulling the cargo trailer on agiven route.

In certain variations, the trailer monitoring system 300 can alsoinclude a bogie position controller 320 that can operate one or morebogie motors to change the position of the rear bogie in relation to thecargo carrier portion of the cargo trailer. For example, the bogieposition controller 320 can receive information indicating the cargoweight and/or weight distribution of the cargo from the sensor dataanalysis engine 330, a local data storage 340 of the trailer monitoringsystem 300, or from a remote resource via a communication interface 350and network connection 360, as described herein. In some examples, thetrailer monitoring system 300 can perform a lookup in the local datastorage 340 or a remote resource via the communication interface 350 andnetwork 360 to determine additional trailer specific information, suchas any additional features of the trailer (e.g., boat fairings on therear doors), the current center of gravity of the trailer, the measuredmass of the trailer, tire information, and various configurationparameters that the trailer monitoring system 300 can check to confirmthe determined parameters.

Once compiled, the trailer monitoring system 300 can transmit the fullsuite of trailer configuration parameters to a communication interface375 of the motion planning engine 370. The trajectory optimizer 380 canprocess the trailer configuration parameters to determine a set oftrajectory instructions that can encompass the motion planning model tobe executed by the motion planning engine 370 during autonomousoperation of the tractor. Accordingly, the control commands ultimatelyexecuted by the various control systems of the tractor can be generatedin accordance with a most optimal set of trajectory instructions giventhe current configuration parameters of the cargo trailer. As a generalexample, in performing 90 degree turns, the updated motion planningmodel will cause the control systems of the tractor to travel along awide trajectory to ensure adequate clearance of the side of the trailerto a nearby sidewalk, traffic signal post, and nearby vehicles, and tofurther ensure that the rear bogie follows a safe practical trajectoryaround the turn (e.g., not running over any curbs).

Methodology

FIG. 4 is a flow chart describing an example method of configuring amotion planning model for autonomous operation of a self-drivingtractor, according to examples described herein. In the belowdescription of FIG. 4, reference may be made to reference charactersrepresenting like features as shown and described with respect to FIGS.1 through 3. Furthermore, the processes described with respect to FIG. 4may be performed by an example autonomous vehicle control system 220 ofa self-driving tractor 200 implementing trailer monitoring and motionplanning systems as described throughout the present disclosure.

Referring to FIG. 4, the control system 220 of the self-driving tractor200 can determine the trailer configuration parameters of a cargotrailer coupled to the fifth wheel of the tractor (400). The controlsystem 220 may then configure a motion planning model for autonomousoperation of the tractor 200 based on the trailer configurationparameters (405). The control system 220 may then autonomously controlthe tractor 200 in accordance with the configured motion planning model(410).

FIG. 5 is a flow chart also describing an example method of configuringa motion planning model for autonomous operation of a self-drivingtractor, according to examples described herein. In the belowdescription of FIG. 5, reference may also be made to referencecharacters representing like features as shown and described withrespect to FIGS. 1 through 3. Furthermore, the processes described withrespect to FIG. 5 may also be performed by an example autonomous vehiclecontrol system 220 of a self-driving tractor 200 implementing trailermonitoring and motion planning systems as described throughout thepresent disclosure.

Referring to FIG. 5, the control system 220 can detect a cargo trailerkingpin coupling to the fifth wheel of the tractor 200 (500). Thecontrol system 220 can access sensor data from sensor resources of thetractor 200 to determine the trailer configuration parameters of thecargo trailer (510). For example, the control system 220 can determinethe dimensions (e.g., length, width, and height) and/or shapecharacteristics of the cargo trailer based on the sensor data (515).Additionally or alternatively, the control system 220 can determine therear bogie position and/or axles positions of the cargo trailer based onthe sensor data (520). In further examples, the control system 220 candetermine the trailer type, such as whether the cargo trailer is astandard box trailer, a car carrier, a tank trailer, a hazardousmaterials trailer, a double trailer, a flatbed, etc. (525).

In certain examples, the control system 220 can also receive orotherwise determine identifying information corresponding to the cargotrailer (530). For example, the control system 220 can identify thecargo trailer's license plate or vehicle identification number in thesensor data. In variations, the control system 220 can receive theidentifying information from a remote resource or the cargo traileritself (e.g., via a power connection providing access to a memoryresource of the trailer. The control system 220 may then perform alookup of the configuration parameters of the cargo trailer based on theidentifying information (535). For example, the control system 220 canperform the lookup in a remote trailer coordination resource or in alocal database comprising updated information of cargo trailers.

According to various implementations, the control system 220 canconfigure the motion planning model for autonomously operating thetractor 200 based on the trailer configuration parameters (540). Forexample, the control system 220 can configure the turningcharacteristics of the tractor 200 based on the individual configurationparameters of the cargo trailer (545). Additionally or alternatively,the control system 220 can configure following and stopping distancesbased on the trailer configuration parameters (550). In furtherexamples, the control system 220 can configure the docking proceduresfor the combined tractor 200 and cargo trailer (555). Still further, thecontrol system 220 can block or add permissible road network areasthrough which the tractor 200 can travel based on the cargo trailer type(e.g., blocking residential neighborhood for hazardous materials cargo)(560).

In additional variations, the control system 220 can configure theclearance parameters of the cargo trailer based on the trailerconfiguration parameters (565). For example, the clearance parametersmay be based on the cargo load (e.g., an oversized load), the cargoitself (e.g., having greater space cushions for tanker cargo, such asgasoline). Once the motion planning model is configured to becustom-tailored for the coupled cargo trailer, the control system 220can operate the tractor 200 in accordance with the configured motionplanning model (570). During autonomous operation, the control system220 can monitor the cargo trailer 220 to, for example, ensure theaccuracy of the motion planning model (575). In one example, the controlsystem 220 monitors the trailing angle of the cargo trailer.

Example Hardware Diagram

FIG. 6 is a block diagram illustrating a computer system upon whichexample processing systems of a self-driving tractor described hereinmay be implemented. The computer system 600 can be implemented using anumber of processing resources 610, which can comprise computerprocessing (CPUs) 611 and field programmable gate arrays (FPGAs) 613. Insome aspects, any number of processors 611 and/or FPGAs 613 of thecomputer system 600 can be utilized as components of a neural networkarray 612 implementing a machine learning model and utilizing roadnetwork maps stored in memory 661 of the computer system 600. In thecontext of FIGS. 2 and 3, various aspects and components of the controlsystem 220, trailer monitoring system 280, 300, and motion planningengine 260,370 can be implemented using one or more components of thecomputer system 600 shown in FIG. 6.

According to some examples, the computer system 600 may be implementedwithin a self-driving tractor with software and hardware resources suchas described with examples of FIGS. 2 and 3. In an example shown, thecomputer system 600 can be distributed spatially into various regions ofthe self-driving tractor, with various aspects integrated with othercomponents of the tractor itself. For example, the processing resources610 and/or memory resources 660 can be provided in a cargo space of theself-driving tractor. The various processing resources 610 of thecomputer system 600 can also execute control instructions 662 usingmicroprocessors 611, FPGAs 613, a neural network array 612, or anycombination of the foregoing.

In an example of FIG. 6, the computer system 600 can include acommunication interface 650 that can enable communications over anetwork 680. In one implementation, the communication interface 650 canalso provide a data bus or other local links to electro-mechanicalinterfaces of the vehicle, such as wireless or wired links to and fromcontrol mechanisms 620 (e.g., via a control interface 621), sensorsystems 630, and can further provide a network link to a backendtransport management system or a remote teleassistance system(implemented on one or more datacenters) over one or more networks 680.

The memory resources 660 can include, for example, main memory 661, aread-only memory (ROM) 667, storage device, and cache resources. Themain memory 661 of memory resources 660 can include random access memory(RAM) 668 or other dynamic storage device, for storing information andinstructions which are executable by the processing resources 610 of thecomputer system 600. The processing resources 610 can executeinstructions for processing information stored with the main memory 661of the memory resources 660. The main memory 661 can also storetemporary variables or other intermediate information which can be usedduring execution of instructions by the processing resources 610. Thememory resources 660 can also include ROM 667 or other static storagedevice for storing static information and instructions for theprocessing resources 610. The memory resources 660 can also includeother forms of memory devices and components, such as a magnetic disk oroptical disk, for purpose of storing information and instructions foruse by the processing resources 610. The computer system 600 can furtherbe implemented using any combination of volatile and/or non-volatilememory, such as flash memory, PROM, EPROM, EEPROM (e.g., storingfirmware 669), DRAM, cache resources, hard disk drives, and/or solidstate drives.

The memory 661 may also store localization maps 664 in which theprocessing resources 610—executing control instructions 662—continuouslycompare to sensor data from the various sensor systems 630 of theself-driving tractor. Execution of the control instructions 662 cancause the processing resources 610 to generate control commands in orderto autonomously operate the tractor's acceleration 622, braking 624,steering 626, and signaling systems 628 (collectively, the controlmechanisms 620). Thus, in executing the control instructions 662, theprocessing resources 610 can receive sensor data from the sensor systems630, dynamically compare the sensor data to a current localization map664, and generate control commands for operative control over theacceleration, steering, and braking of the AV along a particular routeplan. The processing resources 610 may then transmit the controlcommands to one or more control interfaces 621 of the control mechanisms620 to autonomously operate the self-driving tractor along an autonomyroute.

The memory may also store a motion planning model 666 that determinesthat manner in which the processing resources 610 generate controlcommands for execution by the control mechanisms 620 of the tractor. Asdescribed herein, the processing resources 610 can configure, modify, orgenerate the motion planning model 666 based on the trailerconfiguration parameters of a coupled cargo trailer, as describedherein.

While examples of FIG. 6 provide for computing systems for implementingaspects described, some or all of the functionality described withrespect to one computing system of FIG. 6 may be performed by othercomputing systems described with respect to FIG. 6.

It is contemplated for examples described herein to extend to individualelements and concepts described herein, independently of other concepts,ideas or systems, as well as for examples to include combinations ofelements recited anywhere in this application. Although examples aredescribed in detail herein with reference to the accompanying drawings,it is to be understood that the concepts are not limited to thoseprecise examples. As such, many modifications and variations will beapparent to practitioners skilled in this art. Accordingly, it isintended that the scope of the concepts be defined by the followingclaims and their equivalents. Furthermore, it is contemplated that aparticular feature described either individually or as part of anexample can be combined with other individually described features, orparts of other examples, even if the other features and examples make nomentioned of the particular feature. Thus, the absence of describingcombinations should not preclude claiming rights to such combinations.

1.-20. (canceled)
 21. A control system comprising one or more processorsconfigured to execute instructions to cause the control system to:obtain a plurality of trailer parameters of a cargo trailer coupled to aself-driving tractor, wherein the plurality of trailer parameters aredescriptive of a cargo within the cargo trailer; generate a trajectoryfor the self-driving tractor based on the plurality of trailerparameters; and generate a control command for at least one vehiclecontrol mechanism of the self-driving tractor based on the trajectory.22. The control system of claim 21, wherein generating the trajectoryfor the self-driving tractor based on the plurality of trailerparameters comprises: determining a clearance parameter for theself-driving tractor based on the plurality of trailer parameters; andgenerating the trajectory for the self-driving tractor based on theclearance parameter.
 23. The control system of claim 22, wherein theclearance parameter comprises at least one of: (i) a distance betweenthe self-driving tractor and other objects, (ii) a turning radius forthe self-driving tractor, or (iii) a speed of the self-driving tractor.24. The control system of claim 22, wherein the cargo comprises ahazardous material and the clearance parameter for the self-drivingtractor is increased based on the hazardous material.
 25. The controlsystem of claim 21, wherein the plurality of trailer parameters of thecargo trailer are indicative of an arrangement of the cargo within thecargo trailer, and the trajectory for the self-driving tractor isgenerated based on the arrangement of the cargo within the cargotrailer.
 26. The control system of claim 21, wherein the plurality oftrailer parameters of the cargo trailer are indicative of a type oftrailer, and the trajectory for the self-driving tractor is generatedbased on the type of trailer.
 27. The control system of claim 26,wherein the type of trailer comprises at least one of a refrigeratedtrailer, an auto-carrier trailer, a flat-bed trailer, or a hazardousmaterials trailer.
 28. The control system of claim 21, wherein theplurality of trailer parameters of the cargo trailer are indicative of acargo size and the trajectory for the self-driving tractor is generatedbased on the cargo size.
 29. The control system of claim 28, wherein thecargo size is indicative of an oversized load and a clearance parameterfor the self-driving tractor is increased based on the oversized load.30. The control system of claim 21, wherein the instructions furthercause the control system to: monitor one or more characteristics of thecargo trailer during autonomous operation of the self-driving tractor;and update the trajectory based on the one or more characteristics ofthe cargo trailer during autonomous operation of the self-drivingtractor.
 31. A self-driving tractor for a semi-trailer truck,comprising: a plurality of control mechanisms; and a control systemcomprising processing resources executing instructions that cause thecontrol system to: obtain a plurality of trailer parameters of a cargotrailer coupled to the self-driving tractor, wherein the plurality oftrailer parameters are descriptive of a cargo within the cargo trailer;generate a trajectory for the self-driving tractor based on theplurality of trailer parameters; and generate a control command for atleast one of the plurality of control mechanisms of the self-drivingtractor based on the trajectory.
 32. The self-driving tractor of claim31, wherein generating the trajectory for the self-driving tractor basedon the cargo within the cargo trailer comprises: determining a clearanceparameter for the self-driving tractor based on the plurality of trailerparameters; and generating the trajectory for the self-driving tractorbased on the clearance parameter.
 33. The self-driving tractor of claim32, wherein the clearance parameter comprises at least one of: (i) adistance between the self-driving tractor and other objects, (ii) aturning radius for the self-driving tractor, or (iii) a speed of theself-driving tractor.
 34. The self-driving tractor of claim 32, whereinthe cargo comprises a hazardous material and the clearance parameter forthe self-driving tractor is increased based on the hazardous material.35. The self-driving tractor of claim 31, wherein the plurality oftrailer parameters of the cargo trailer are indicative of an arrangementof the cargo within the cargo trailer, and the trajectory for theself-driving tractor is generated based on the arrangement of the cargowithin the cargo trailer.
 36. The self-driving tractor of claim 31,wherein the plurality of trailer parameters of the cargo trailer areindicative of a type of trailer, and the trajectory for the self-drivingtractor is generated based on the type of trailer.
 37. The self-drivingtractor of claim 36, wherein the type of trailer comprises at least oneof a refrigerated trailer, an auto-carrier trailer, a flat-bed trailer,or a hazardous materials trailer.
 38. A computer-implemented method forcontrolling a self-driving tractor, the method comprising: obtaining aplurality of trailer parameters of a cargo trailer coupled to aself-driving tractor, wherein the plurality of trailer parameters aredescriptive of a cargo within the cargo trailer; generating a trajectoryfor the self-driving tractor based on the plurality of trailerparameters; and generating a control command for at least one vehiclecontrol mechanism of the self-driving tractor based on the trajectory.39. The computer-implemented method of claim 38, wherein the pluralityof trailer parameters of the cargo trailer are indicative of a cargosize and the trajectory for the self-driving tractor is generated basedon the cargo size.
 40. The computer-implemented method of claim 39,wherein the cargo size is indicative of an oversized load and aclearance parameter for the self-driving tractor is increased based onthe oversized load.